import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

# from model import *

# 定义训练设备

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 准备数据集
# 训练数据集
train_data = torchvision.datasets.CIFAR10(root='./CIFAR10', train=True, transform=torchvision.transforms.ToTensor(),
                                          download=True)
# 测试数据集
test_data = torchvision.datasets.CIFAR10(root='./CIFAR10', train=False, transform=torchvision.transforms.ToTensor(),
                                         download=True)

# length 长度
train_data_size = len(train_data)
test_data_size = len(test_data)
# 如果train_data_size=10, 训练数据集的长度为：10
print("训练数据集的长度为：{}".format(train_data_size))
print("测试数据集的长度为：{}".format(test_data_size))

# 利用 DataLoader 来加载数据集
train_loader = DataLoader(train_data, batch_size=64)
test_loader = DataLoader(test_data, batch_size=64)


# 创建网络模型
class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.model1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=5, stride=1, padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(64 * 4 * 4, 64),
            nn.Linear(64, 10),
        )

    def forward(self, x):
        x = self.model1(x)
        return x
tudui = Tudui()
tudui.to(device)

# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn.to(device)

# 优化器
learning_rate = 0.01
# 1e-2=1 x (10)^(-2) = 1/100 = 0.0.1
# learning_rate = 1e-2
optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)

# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10

writer = SummaryWriter('./logs/logs_train')

for i in range(epoch):
    print("----------第 {} 轮训练开始-----------".format(i + 1))
    # 训练步骤开始
    tudui.train()
    for data in train_loader:
        imgs, targets = data
        imgs = imgs.to(device)
        targets = targets.to(device)
        outputs = tudui(imgs)
        # 损失
        loss = loss_fn(outputs, targets)
        # 梯度清零
        optimizer.zero_grad()
        # 反向传播
        loss.backward()
        optimizer.step()

        total_train_step += 1
        if total_train_step % 100 == 0:
            print("训练次数：{}，Loss：{}".format(total_train_step, loss.item()))
            writer.add_scalar('train_loss', loss.item(), total_train_step)

    # 测试步骤开始
    tudui.eval()
    total_test_loss = 0
    # 整体正确个数
    total_accuracy = 0
    with torch.no_grad():
        for data in test_loader:
            imgs, targets = data
            imgs = imgs.to(device)
            targets = targets.to(device)
            outputs = tudui(imgs)
            loss = loss_fn(outputs, targets)
            total_test_loss += loss.item()

            accuracy = (outputs.argmax(1) == targets).sum()
            total_accuracy += accuracy

    print("整体测试集上的Loss：{}".format(total_test_loss))
    print("整体测试集上的正确率：{}".format(total_accuracy / test_data_size))
    writer.add_scalar('test_loss', total_test_loss, total_test_step)
    writer.add_scalar('test_accuracy', total_accuracy / test_data_size, total_test_step)
    total_test_step += 1

    # 每轮结束保存模型
    torch.save(tudui.state_dict(), 'tudui_{}.pth'.format(i))
    print("模型已保存")

writer.close()
